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28 July 2023
Growth differentiation factor 15 predicts cardiovascular events in stable coronary artery disease
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Growth differentiation factor 15 (GDF-15) has been explored as a potential biomarker for various inflammatory diseases and cardiovascular events. This study aimed to assess the predictive role of GDF-15 levels in cardiovascular events and all-cause mortality, considering traditional risk factors and other biomarkers.
A prospective study was conducted and 3699 patients with stable coronary artery disease (CAD) were enrolled into the research. Baseline GDF-15 levels were measured. Median follow-up was 3.1 years during the study. We analyzed clinical variables and several biomarkers. Multivariable Cox regression analysis was performed to evaluate prognostic performance of GDF-15 levels in predicting myocardial infarction (MI), heart failure, stroke, cardiovascular death, and non-cardiovascular death.
Baseline GDF-15 levels for 3699 patients were grouped by quartile (≤ 1153, 1153–1888, 1888–3043, > 3043 ng/L). Higher GDF-15 levels were associated with older age, male gender, history of hypertension, and elevated levels of N-terminal pro B-type natriuretic peptide (NT-pro BNP), soluble suppression of tumorigenesis-2 (sST2), and creatine (each with P < 0.001). Adjusting for established risk factors and biomarkers in Cox proportional hazards models, a 1 standard deviation (SD) increase in GDF-15 was associated with elevated risk of clinical events [hazard ratio (HR) = 2.18, 95% confidence interval (CI): (1.52–3.11)], including: MI [HR = 2.83 95% CI: (1.03–7.74)], heart failure [HR = 2.71 95% CI: (1.18–6.23)], cardiovascular and non-cardiovascular death [HR = 2.48, 95% CI (1.49–4.11)] during the median follow up of 3.1 years.
Higher levels of GDF-15 consistently provides prognostic information for cardiovascular events and all cause death, independent of clinical risk factors and other biomarkers. GDF-15 could be considered as a valuable addition to future risk prediction model in secondary prevention for predicting clinical events in patient with stable CAD.
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Growth differentiation factor 15 predicts cardiovascular events in stable coronary artery disease
Abstract
Growth differentiation factor 15 (GDF-15) has been explored as a potential biomarker for various inflammatory diseases and cardiovascular events. This study aimed to assess the predictive role of GDF-15 levels in cardiovascular events and all-cause mortality, considering traditional risk factors and other biomarkers.
A prospective study was conducted and 3699 patients with stable coronary artery disease (CAD) were enrolled into the research. Baseline GDF-15 levels were measured. Median follow-up was 3.1 years during the study. We analyzed clinical variables and several biomarkers. Multivariable Cox regression analysis was performed to evaluate prognostic performance of GDF-15 levels in predicting myocardial infarction (MI), heart failure, stroke, cardiovascular death, and non-cardiovascular death.
Baseline GDF-15 levels for 3699 patients were grouped by quartile (≤ 1153, 1153–1888, 1888–3043, > 3043 ng/L). Higher GDF-15 levels were associated with older age, male gender, history of hypertension, and elevated levels of N-terminal pro B-type natriuretic peptide (NT-pro BNP), soluble suppression of tumorigenesis-2 (sST2), and creatine (each with P < 0.001). Adjusting for established risk factors and biomarkers in Cox proportional hazards models, a 1 standard deviation (SD) increase in GDF-15 was associated with elevated risk of clinical events [hazard ratio (HR) = 2.18, 95% confidence interval (CI): (1.52–3.11)], including: MI [HR = 2.83 95% CI: (1.03–7.74)], heart failure [HR = 2.71 95% CI: (1.18–6.23)], cardiovascular and non-cardiovascular death [HR = 2.48, 95% CI (1.49–4.11)] during the median follow up of 3.1 years.
Higher levels of GDF-15 consistently provides prognostic information for cardiovascular events and all cause death, independent of clinical risk factors and other biomarkers. GDF-15 could be considered as a valuable addition to future risk prediction model in secondary prevention for predicting clinical events in patient with stable CAD.
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